Overview

Dataset statistics

Number of variables21
Number of observations740
Missing cells0
Missing cells (%)0.0%
Duplicate rows26
Duplicate rows (%)3.5%
Total size in memory121.5 KiB
Average record size in memory168.2 B

Variable types

Numeric14
Categorical7

Warnings

Dataset has 26 (3.5%) duplicate rowsDuplicates
Reason for absence is highly correlated with Disciplinary failureHigh correlation
Service time is highly correlated with AgeHigh correlation
Age is highly correlated with Service timeHigh correlation
Disciplinary failure is highly correlated with Reason for absenceHigh correlation
Weight is highly correlated with Body mass indexHigh correlation
Body mass index is highly correlated with WeightHigh correlation
Service time is highly correlated with Age and 2 other fieldsHigh correlation
Age is highly correlated with Service timeHigh correlation
Weight is highly correlated with Service time and 1 other fieldsHigh correlation
Body mass index is highly correlated with Service time and 1 other fieldsHigh correlation
Service time is highly correlated with AgeHigh correlation
Age is highly correlated with Service timeHigh correlation
Weight is highly correlated with Body mass indexHigh correlation
Body mass index is highly correlated with WeightHigh correlation
Age is highly correlated with Height and 9 other fieldsHigh correlation
Height is highly correlated with Age and 10 other fieldsHigh correlation
ID is highly correlated with Age and 10 other fieldsHigh correlation
Son is highly correlated with Age and 8 other fieldsHigh correlation
Seasons is highly correlated with Month of absence and 2 other fieldsHigh correlation
Reason for absence is highly correlated with Disciplinary failureHigh correlation
Pet is highly correlated with Age and 10 other fieldsHigh correlation
Transportation expense is highly correlated with Age and 10 other fieldsHigh correlation
Body mass index is highly correlated with Age and 11 other fieldsHigh correlation
Month of absence is highly correlated with Seasons and 2 other fieldsHigh correlation
Work load Average/day is highly correlated with Seasons and 2 other fieldsHigh correlation
Disciplinary failure is highly correlated with Reason for absenceHigh correlation
Weight is highly correlated with Age and 11 other fieldsHigh correlation
Hit target is highly correlated with Seasons and 2 other fieldsHigh correlation
Social smoker is highly correlated with Height and 5 other fieldsHigh correlation
Distance from Residence to Work is highly correlated with Age and 11 other fieldsHigh correlation
Education is highly correlated with Height and 8 other fieldsHigh correlation
Service time is highly correlated with Age and 10 other fieldsHigh correlation
Social drinker is highly correlated with Age and 8 other fieldsHigh correlation
Reason for absence has 43 (5.8%) zeros Zeros
Pet has 460 (62.2%) zeros Zeros
Absenteeism time in hours has 44 (5.9%) zeros Zeros

Reproduction

Analysis started2021-09-09 15:40:34.666047
Analysis finished2021-09-09 15:41:00.469453
Duration25.8 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct36
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.01756757
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2021-09-09T21:11:00.549383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q19
median18
Q328
95-th percentile34
Maximum36
Range35
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.02124726
Coefficient of variation (CV)0.6116945155
Kurtosis-1.251818317
Mean18.01756757
Median Absolute Deviation (MAD)10
Skewness0.01660590675
Sum13333
Variance121.4678912
MonotonicityNot monotonic
2021-09-09T21:11:00.645382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3113
15.3%
2876
 
10.3%
3455
 
7.4%
2246
 
6.2%
2042
 
5.7%
1140
 
5.4%
1537
 
5.0%
3634
 
4.6%
2430
 
4.1%
1429
 
3.9%
Other values (26)238
32.2%
ValueCountFrequency (%)
123
 
3.1%
26
 
0.8%
3113
15.3%
41
 
0.1%
519
 
2.6%
68
 
1.1%
76
 
0.8%
82
 
0.3%
98
 
1.1%
1024
 
3.2%
ValueCountFrequency (%)
3634
4.6%
351
 
0.1%
3455
7.4%
3324
 
3.2%
325
 
0.7%
313
 
0.4%
307
 
0.9%
295
 
0.7%
2876
10.3%
277
 
0.9%

Reason for absence
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.21621622
Minimum0
Maximum28
Zeros43
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2021-09-09T21:11:00.741380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median23
Q326
95-th percentile28
Maximum28
Range28
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.433405883
Coefficient of variation (CV)0.4388692232
Kurtosis-0.2599250726
Mean19.21621622
Median Absolute Deviation (MAD)5
Skewness-0.9153123659
Sum14220
Variance71.12233478
MonotonicityNot monotonic
2021-09-09T21:11:00.829391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
23149
20.1%
28112
15.1%
2769
9.3%
1355
 
7.4%
043
 
5.8%
1940
 
5.4%
2238
 
5.1%
2633
 
4.5%
2531
 
4.2%
1126
 
3.5%
Other values (18)144
19.5%
ValueCountFrequency (%)
043
5.8%
116
 
2.2%
21
 
0.1%
31
 
0.1%
42
 
0.3%
53
 
0.4%
68
 
1.1%
715
 
2.0%
86
 
0.8%
94
 
0.5%
ValueCountFrequency (%)
28112
15.1%
2769
9.3%
2633
 
4.5%
2531
 
4.2%
243
 
0.4%
23149
20.1%
2238
 
5.1%
216
 
0.8%
1940
 
5.4%
1821
 
2.8%

Month of absence
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.324324324
Minimum0
Maximum12
Zeros3
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2021-09-09T21:11:00.909381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.436286932
Coefficient of variation (CV)0.5433445149
Kurtosis-1.254966504
Mean6.324324324
Median Absolute Deviation (MAD)3
Skewness0.06936854151
Sum4680
Variance11.80806788
MonotonicityNot monotonic
2021-09-09T21:11:00.997389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
387
11.8%
272
9.7%
1071
9.6%
767
9.1%
564
8.6%
1163
8.5%
854
7.3%
654
7.3%
953
7.2%
453
7.2%
Other values (3)102
13.8%
ValueCountFrequency (%)
03
 
0.4%
150
6.8%
272
9.7%
387
11.8%
453
7.2%
564
8.6%
654
7.3%
767
9.1%
854
7.3%
953
7.2%
ValueCountFrequency (%)
1249
6.6%
1163
8.5%
1071
9.6%
953
7.2%
854
7.3%
767
9.1%
654
7.3%
564
8.6%
453
7.2%
387
11.8%

Day of the week
Categorical

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
2
161 
4
156 
3
154 
6
144 
5
125 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters740
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row4
4th row5
5th row5

Common Values

ValueCountFrequency (%)
2161
21.8%
4156
21.1%
3154
20.8%
6144
19.5%
5125
16.9%

Length

2021-09-09T21:11:01.181448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T21:11:01.245382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2161
21.8%
4156
21.1%
3154
20.8%
6144
19.5%
5125
16.9%

Most occurring characters

ValueCountFrequency (%)
2161
21.8%
4156
21.1%
3154
20.8%
6144
19.5%
5125
16.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number740
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2161
21.8%
4156
21.1%
3154
20.8%
6144
19.5%
5125
16.9%

Most occurring scripts

ValueCountFrequency (%)
Common740
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2161
21.8%
4156
21.1%
3154
20.8%
6144
19.5%
5125
16.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2161
21.8%
4156
21.1%
3154
20.8%
6144
19.5%
5125
16.9%

Seasons
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
4
195 
2
192 
3
183 
1
170 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters740
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
4195
26.4%
2192
25.9%
3183
24.7%
1170
23.0%

Length

2021-09-09T21:11:01.445429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T21:11:01.501495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
4195
26.4%
2192
25.9%
3183
24.7%
1170
23.0%

Most occurring characters

ValueCountFrequency (%)
4195
26.4%
2192
25.9%
3183
24.7%
1170
23.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number740
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4195
26.4%
2192
25.9%
3183
24.7%
1170
23.0%

Most occurring scripts

ValueCountFrequency (%)
Common740
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4195
26.4%
2192
25.9%
3183
24.7%
1170
23.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4195
26.4%
2192
25.9%
3183
24.7%
1170
23.0%

Transportation expense
Real number (ℝ≥0)

HIGH CORRELATION

Distinct24
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221.3297297
Minimum118
Maximum388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2021-09-09T21:11:01.581465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum118
5-th percentile118
Q1179
median225
Q3260
95-th percentile361
Maximum388
Range270
Interquartile range (IQR)81

Descriptive statistics

Standard deviation66.95222325
Coefficient of variation (CV)0.3024999096
Kurtosis-0.3182910179
Mean221.3297297
Median Absolute Deviation (MAD)46
Skewness0.396188637
Sum163784
Variance4482.600197
MonotonicityNot monotonic
2021-09-09T21:11:01.677448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
179180
24.3%
11892
12.4%
22581
10.9%
23558
 
7.8%
28945
 
6.1%
26042
 
5.7%
29140
 
5.4%
24630
 
4.1%
15529
 
3.9%
24824
 
3.2%
Other values (14)119
16.1%
ValueCountFrequency (%)
11892
12.4%
15529
 
3.9%
1577
 
0.9%
179180
24.3%
1847
 
0.9%
1898
 
1.1%
22581
10.9%
2288
 
1.1%
2312
 
0.3%
2337
 
0.9%
ValueCountFrequency (%)
3883
 
0.4%
3788
 
1.1%
36915
 
2.0%
36124
3.2%
33016
 
2.2%
3005
 
0.7%
29140
5.4%
28945
6.1%
2796
 
0.8%
2683
 
0.4%

Distance from Residence to Work
Real number (ℝ≥0)

HIGH CORRELATION

Distinct25
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.63108108
Minimum5
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2021-09-09T21:11:01.765465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10
Q116
median26
Q350
95-th percentile51
Maximum52
Range47
Interquartile range (IQR)34

Descriptive statistics

Standard deviation14.83678844
Coefficient of variation (CV)0.5007170814
Kurtosis-1.261682574
Mean29.63108108
Median Absolute Deviation (MAD)11
Skewness0.3120827847
Sum21927
Variance220.1302911
MonotonicityNot monotonic
2021-09-09T21:11:01.853407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
26128
17.3%
51120
16.2%
1055
 
7.4%
2554
 
7.3%
5045
 
6.1%
3640
 
5.4%
3137
 
5.0%
1334
 
4.6%
1229
 
3.9%
1126
 
3.5%
Other values (15)172
23.2%
ValueCountFrequency (%)
56
 
0.8%
1055
7.4%
1126
3.5%
1229
3.9%
1334
4.6%
149
 
1.2%
159
 
1.2%
1626
3.5%
1715
 
2.0%
2019
 
2.6%
ValueCountFrequency (%)
5224
 
3.2%
51120
16.2%
5045
 
6.1%
498
 
1.1%
485
 
0.7%
451
 
0.1%
427
 
0.9%
3640
 
5.4%
352
 
0.3%
3137
 
5.0%

Service time
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.55405405
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2021-09-09T21:11:01.941378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q19
median13
Q316
95-th percentile18
Maximum29
Range28
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.384873408
Coefficient of variation (CV)0.3492794749
Kurtosis0.6831107752
Mean12.55405405
Median Absolute Deviation (MAD)4
Skewness-0.004719563328
Sum9290
Variance19.2271148
MonotonicityNot monotonic
2021-09-09T21:11:02.021379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
18147
19.9%
9126
17.0%
1485
11.5%
1373
9.9%
1261
8.2%
1055
 
7.4%
1150
 
6.8%
1638
 
5.1%
324
 
3.2%
1720
 
2.7%
Other values (8)61
8.2%
ValueCountFrequency (%)
17
 
0.9%
324
 
3.2%
416
 
2.2%
67
 
0.9%
77
 
0.9%
813
 
1.8%
9126
17.0%
1055
7.4%
1150
 
6.8%
1261
8.2%
ValueCountFrequency (%)
295
 
0.7%
242
 
0.3%
18147
19.9%
1720
 
2.7%
1638
 
5.1%
154
 
0.5%
1485
11.5%
1373
9.9%
1261
8.2%
1150
 
6.8%

Age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct22
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.45
Minimum27
Maximum58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2021-09-09T21:11:02.117379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile28
Q131
median37
Q340
95-th percentile50
Maximum58
Range31
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.478772458
Coefficient of variation (CV)0.1777441003
Kurtosis0.4316130531
Mean36.45
Median Absolute Deviation (MAD)4
Skewness0.6977034092
Sum26973
Variance41.97449256
MonotonicityNot monotonic
2021-09-09T21:11:02.397433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
28117
15.8%
38113
15.3%
3778
10.5%
4058
7.8%
3351
6.9%
3650
 
6.8%
3046
 
6.2%
5037
 
5.0%
4134
 
4.6%
3429
 
3.9%
Other values (12)127
17.2%
ValueCountFrequency (%)
277
 
0.9%
28117
15.8%
297
 
0.9%
3046
 
6.2%
3122
 
3.0%
3213
 
1.8%
3351
6.9%
3429
 
3.9%
3650
6.8%
3778
10.5%
ValueCountFrequency (%)
588
 
1.1%
531
 
0.1%
5037
5.0%
495
 
0.7%
486
 
0.8%
4724
3.2%
462
 
0.3%
4324
3.2%
4134
4.6%
4058
7.8%

Work load Average/day
Real number (ℝ≥0)

HIGH CORRELATION

Distinct38
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271.4902351
Minimum205.917
Maximum378.884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2021-09-09T21:11:02.493423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum205.917
5-th percentile222.196
Q1244.387
median264.249
Q3294.217
95-th percentile343.253
Maximum378.884
Range172.967
Interquartile range (IQR)49.83

Descriptive statistics

Standard deviation39.05811619
Coefficient of variation (CV)0.1438656391
Kurtosis0.6181879633
Mean271.4902351
Median Absolute Deviation (MAD)20.604
Skewness0.9614566084
Sum200902.774
Variance1525.53644
MonotonicityNot monotonic
2021-09-09T21:11:02.589376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
222.19636
 
4.9%
264.24933
 
4.5%
237.65632
 
4.3%
343.25329
 
3.9%
265.01728
 
3.8%
284.85325
 
3.4%
308.59324
 
3.2%
268.51923
 
3.1%
284.03122
 
3.0%
244.38722
 
3.0%
Other values (28)466
63.0%
ValueCountFrequency (%)
205.91721
2.8%
222.19636
4.9%
230.2920
2.7%
236.62919
2.6%
237.65632
4.3%
239.40913
 
1.8%
239.55419
2.6%
241.47622
3.0%
244.38722
3.0%
246.07416
2.2%
ValueCountFrequency (%)
378.88416
2.2%
377.5516
2.2%
343.25329
3.9%
330.06111
 
1.5%
326.45220
2.7%
313.53215
2.0%
308.59324
3.2%
306.34518
2.4%
302.58518
2.4%
294.21719
2.6%

Hit target
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.58783784
Minimum81
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2021-09-09T21:11:02.685406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum81
5-th percentile88
Q193
median95
Q397
95-th percentile99
Maximum100
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.779313134
Coefficient of variation (CV)0.03995559282
Kurtosis2.419042289
Mean94.58783784
Median Absolute Deviation (MAD)2
Skewness-1.261708179
Sum69995
Variance14.28320777
MonotonicityNot monotonic
2021-09-09T21:11:02.765385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
93105
14.2%
99102
13.8%
9789
12.0%
9279
10.7%
9675
10.1%
9575
10.1%
9866
8.9%
9145
6.1%
9434
 
4.6%
8828
 
3.8%
Other values (3)42
 
5.7%
ValueCountFrequency (%)
8119
 
2.6%
8712
 
1.6%
8828
 
3.8%
9145
6.1%
9279
10.7%
93105
14.2%
9434
 
4.6%
9575
10.1%
9675
10.1%
9789
12.0%
ValueCountFrequency (%)
10011
 
1.5%
99102
13.8%
9866
8.9%
9789
12.0%
9675
10.1%
9575
10.1%
9434
 
4.6%
93105
14.2%
9279
10.7%
9145
6.1%

Disciplinary failure
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
700 
1
 
40

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters740
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0700
94.6%
140
 
5.4%

Length

2021-09-09T21:11:02.965446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T21:11:03.021462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0700
94.6%
140
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0700
94.6%
140
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number740
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0700
94.6%
140
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common740
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0700
94.6%
140
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0700
94.6%
140
 
5.4%

Education
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
1
611 
3
79 
2
 
46
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters740
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1611
82.6%
379
 
10.7%
246
 
6.2%
44
 
0.5%

Length

2021-09-09T21:11:03.181452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T21:11:03.245378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1611
82.6%
379
 
10.7%
246
 
6.2%
44
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1611
82.6%
379
 
10.7%
246
 
6.2%
44
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number740
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1611
82.6%
379
 
10.7%
246
 
6.2%
44
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common740
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1611
82.6%
379
 
10.7%
246
 
6.2%
44
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1611
82.6%
379
 
10.7%
246
 
6.2%
44
 
0.5%

Son
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
298 
1
229 
2
156 
4
42 
3
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters740
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row0
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0298
40.3%
1229
30.9%
2156
21.1%
442
 
5.7%
315
 
2.0%

Length

2021-09-09T21:11:03.437375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T21:11:03.509382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0298
40.3%
1229
30.9%
2156
21.1%
442
 
5.7%
315
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0298
40.3%
1229
30.9%
2156
21.1%
442
 
5.7%
315
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number740
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0298
40.3%
1229
30.9%
2156
21.1%
442
 
5.7%
315
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common740
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0298
40.3%
1229
30.9%
2156
21.1%
442
 
5.7%
315
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0298
40.3%
1229
30.9%
2156
21.1%
442
 
5.7%
315
 
2.0%

Social drinker
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
1
420 
0
320 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters740
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1420
56.8%
0320
43.2%

Length

2021-09-09T21:11:03.677376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T21:11:03.741374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1420
56.8%
0320
43.2%

Most occurring characters

ValueCountFrequency (%)
1420
56.8%
0320
43.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number740
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1420
56.8%
0320
43.2%

Most occurring scripts

ValueCountFrequency (%)
Common740
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1420
56.8%
0320
43.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1420
56.8%
0320
43.2%

Social smoker
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
686 
1
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters740
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0686
92.7%
154
 
7.3%

Length

2021-09-09T21:11:03.893374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-09T21:11:03.949379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0686
92.7%
154
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0686
92.7%
154
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number740
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0686
92.7%
154
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common740
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0686
92.7%
154
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0686
92.7%
154
 
7.3%

Pet
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7459459459
Minimum0
Maximum8
Zeros460
Zeros (%)62.2%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2021-09-09T21:11:03.997451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.318258291
Coefficient of variation (CV)1.767230318
Kurtosis9.67482687
Mean0.7459459459
Median Absolute Deviation (MAD)0
Skewness2.735715439
Sum552
Variance1.737804923
MonotonicityNot monotonic
2021-09-09T21:11:04.069374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0460
62.2%
1138
 
18.6%
296
 
13.0%
432
 
4.3%
88
 
1.1%
56
 
0.8%
ValueCountFrequency (%)
0460
62.2%
1138
 
18.6%
296
 
13.0%
432
 
4.3%
56
 
0.8%
88
 
1.1%
ValueCountFrequency (%)
88
 
1.1%
56
 
0.8%
432
 
4.3%
296
 
13.0%
1138
 
18.6%
0460
62.2%

Weight
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.03513514
Minimum56
Maximum108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2021-09-09T21:11:04.149373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile56
Q169
median83
Q389
95-th percentile98
Maximum108
Range52
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.88321051
Coefficient of variation (CV)0.1630061173
Kurtosis-0.9139276214
Mean79.03513514
Median Absolute Deviation (MAD)11
Skewness0.01700137215
Sum58486
Variance165.977113
MonotonicityNot monotonic
2021-09-09T21:11:04.245381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
89113
15.3%
6985
11.5%
6561
 
8.2%
8355
 
7.4%
5646
 
6.2%
9040
 
5.4%
7337
 
5.0%
9835
 
4.7%
6730
 
4.1%
9529
 
3.9%
Other values (16)209
28.2%
ValueCountFrequency (%)
5646
6.2%
587
 
0.9%
6320
 
2.7%
6561
8.2%
6730
 
4.1%
6813
 
1.8%
6985
11.5%
7015
 
2.0%
7337
5.0%
7519
 
2.6%
ValueCountFrequency (%)
1085
 
0.7%
10619
 
2.6%
1002
 
0.3%
9835
 
4.7%
9529
 
3.9%
944
 
0.5%
9040
 
5.4%
89113
15.3%
8829
 
3.9%
8624
 
3.2%

Height
Real number (ℝ≥0)

HIGH CORRELATION

Distinct14
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.1148649
Minimum163
Maximum196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2021-09-09T21:11:04.333452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum163
5-th percentile167
Q1169
median170
Q3172
95-th percentile182
Maximum196
Range33
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.03499453
Coefficient of variation (CV)0.03506376126
Kurtosis7.317235494
Mean172.1148649
Median Absolute Deviation (MAD)2
Skewness2.566059693
Sum127365
Variance36.42115898
MonotonicityNot monotonic
2021-09-09T21:11:04.413444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
170166
22.4%
172155
20.9%
16995
12.8%
17183
11.2%
17857
 
7.7%
16848
 
6.5%
16734
 
4.6%
19629
 
3.9%
16524
 
3.2%
18220
 
2.7%
Other values (4)29
 
3.9%
ValueCountFrequency (%)
1636
 
0.8%
16524
 
3.2%
16734
 
4.6%
16848
 
6.5%
16995
12.8%
170166
22.4%
17183
11.2%
172155
20.9%
1748
 
1.1%
1758
 
1.1%
ValueCountFrequency (%)
19629
 
3.9%
1857
 
0.9%
18220
 
2.7%
17857
 
7.7%
1758
 
1.1%
1748
 
1.1%
172155
20.9%
17183
11.2%
170166
22.4%
16995
12.8%

Body mass index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.67702703
Minimum19
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2021-09-09T21:11:04.501461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile19
Q124
median25
Q331
95-th percentile32
Maximum38
Range19
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.285452223
Coefficient of variation (CV)0.1606420468
Kurtosis-0.3143750012
Mean26.67702703
Median Absolute Deviation (MAD)3
Skewness0.3050456554
Sum19741
Variance18.36510076
MonotonicityNot monotonic
2021-09-09T21:11:04.589480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
31147
19.9%
25126
17.0%
2486
11.6%
2375
10.1%
2859
8.0%
1946
 
6.2%
3040
 
5.4%
2235
 
4.7%
2724
 
3.2%
3224
 
3.2%
Other values (7)78
10.5%
ValueCountFrequency (%)
1946
 
6.2%
2122
 
3.0%
2235
 
4.7%
2375
10.1%
2486
11.6%
25126
17.0%
2724
 
3.2%
2859
8.0%
2923
 
3.1%
3040
 
5.4%
ValueCountFrequency (%)
3819
 
2.6%
365
 
0.7%
352
 
0.3%
341
 
0.1%
336
 
0.8%
3224
 
3.2%
31147
19.9%
3040
 
5.4%
2923
 
3.1%
2859
8.0%

Absenteeism time in hours
Real number (ℝ≥0)

ZEROS

Distinct19
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.924324324
Minimum0
Maximum120
Zeros44
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2021-09-09T21:11:04.685419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q38
95-th percentile24
Maximum120
Range120
Interquartile range (IQR)6

Descriptive statistics

Standard deviation13.3309981
Coefficient of variation (CV)1.925241724
Kurtosis38.77730708
Mean6.924324324
Median Absolute Deviation (MAD)2
Skewness5.720727863
Sum5124
Variance177.7155104
MonotonicityNot monotonic
2021-09-09T21:11:04.773494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
8208
28.1%
2157
21.2%
3112
15.1%
188
11.9%
460
 
8.1%
044
 
5.9%
1619
 
2.6%
2416
 
2.2%
407
 
0.9%
57
 
0.9%
Other values (9)22
 
3.0%
ValueCountFrequency (%)
044
 
5.9%
188
11.9%
2157
21.2%
3112
15.1%
460
 
8.1%
57
 
0.9%
71
 
0.1%
8208
28.1%
1619
 
2.6%
2416
 
2.2%
ValueCountFrequency (%)
1203
 
0.4%
1122
 
0.3%
1041
 
0.1%
803
 
0.4%
643
 
0.4%
562
 
0.3%
481
 
0.1%
407
0.9%
326
 
0.8%
2416
2.2%

Interactions

2021-09-09T21:10:37.477426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:37.581425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:37.677427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:37.773428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:37.877427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:37.973427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:38.069427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:38.165426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:38.277493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:38.885424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:38.989494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:39.109433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:39.237423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:39.349514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:39.461427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:39.565497image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:39.669504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:39.829432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:39.949492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:40.037421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:40.141496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:40.261431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:40.381491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:40.485490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:40.589508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:40.701474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:40.821422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:40.933489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:41.037502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-09T21:10:41.149495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

2021-09-09T21:11:04.877372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-09T21:11:05.141453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-09T21:11:05.397457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-09T21:11:05.637450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-09T21:11:05.869372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-09T21:10:59.877381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-09T21:11:00.317461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

IDReason for absenceMonth of absenceDay of the weekSeasonsTransportation expenseDistance from Residence to WorkService timeAgeWork load Average/dayHit targetDisciplinary failureEducationSonSocial drinkerSocial smokerPetWeightHeightBody mass indexAbsenteeism time in hours
01126731289361333239.5549701210190172304
1360731118131850239.5549711110098178310
2323741179511838239.5549701010089170312
37775127951439239.5549701211068168244
41123751289361333239.5549701210190172302
5323761179511838239.5549701010089170312
6102276136152328239.5549701110480172278
72023761260501136239.5549701410065168234
81419721155121434239.55497012100951962540
9122721235111437239.5549703100188172298

Last rows

IDReason for absenceMonth of absenceDay of the weekSeasonsTransportation expenseDistance from Residence to WorkService timeAgeWork load Average/dayHit targetDisciplinary failureEducationSonSocial drinkerSocial smokerPetWeightHeightBody mass indexAbsenteeism time in hours
730622731189291333264.60493012002691672516
7313423741118101037264.6049301000083172282
732102274136152328264.6049301110480172278
733282274122526928264.6049301100269169248
7341313721369171231264.60493013100701692580
7351114731289361333264.6049301210190172308
736111731235111437264.6049303100188172294
73740031118141340271.2199501110898170340
73880042231351439271.21995012102100170350
739350063179451453271.2199501100177175250

Duplicate rows

Most frequently occurring

IDReason for absenceMonth of absenceDay of the weekSeasonsTransportation expenseDistance from Residence to WorkService timeAgeWork load Average/dayHit targetDisciplinary failureEducationSonSocial drinkerSocial smokerPetWeightHeightBody mass indexAbsenteeism time in hours# duplicates
2327242179511838251.81896010100891703134
14222746317926930246.28891030000561711924
3327242179511838264.24997010100891703123
5327262179511838251.81896010100891703133
7327342179511838222.19699010100891703123
8327352179511838222.19699010100891703133
0323761179511838239.55497010100891703122
1327222179511838264.24997010100891703122
4327252179511838264.24997010100891703122
6327262179511838264.24997010100891703122